Learning Automata - A Survey

Narendra, Kumpati S.; Thathachar, M. A. L.

doi:10.1109/tsmc.1974.5408453

Cited by 597 publications

(267 citation statements)

References 35 publications

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“…They have been used in game playing [6,7], parameter optimization [8,9], vehicle path control [10], channel selection in cognitive radio networks [11], assigning capacities in prioritized networks [12], and resource allocation [13]. LA have also been used in natural language processing, string taxonomy [14], graph patitioning [15], and map learning [16].…”

Section: Learning Automata and Their Applicationsmentioning

confidence: 99%

Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning

Zhang

Granmo

Oommen

2012

Advanced Research in Applied Artificial Intelligence

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Abstract. The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction (L RI )-like schemes, can be explained by their ability to pursue the actions with the highest reward probability estimates. Without access to reward probability estimates, it makes sense for schemes like the L RI to first make large exploring steps, and then to gradually turn exploration into exploitation by making progressively smaller learning steps. However, this behavior becomes counter-intuitive when pursuing actions based on their estimated reward probabilities. Learning should then ideally proceed in progressively larger steps, as the reward probability estimates turn more accurate. This paper introduces a new estimator algorithm, the Discretized Bayesian Pursuit Algorithm (DBPA), that achieves this. The DBPA is implemented by linearly discretizing the action probability space of the Bayesian Pursuit Algorithm (BPA) [1]. The key innovation is that the linear discrete updating rules mitigate the counter-intuitive behavior of the corresponding linear continuous updating rules, by augmenting them with the reward probability estimates. Extensive experimental results show the superiority of DBPA over previous estimator algorithms. Indeed, the DBPA is probably the fastest reported LA to date.

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Section: Learning Automata and Their Applicationsmentioning

confidence: 99%

Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning

Zhang

Granmo

Oommen

2012

Advanced Research in Applied Artificial Intelligence

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“…5, October 2012 561 finite state automata. Learning automata select their current action based on past experiences from the environment [29]. A learning automaton is an adaptive decision-making unit situated in a random environment that learns the optimal action through repeated interactions with its environment.…”

Section: Taxonomy Of Supervised Learning Algorithmsmentioning

confidence: 99%

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

Hormozi¹,

Hormozi²,

Nohooji³

2012

IJMLC

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Abstract-Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques used in robotic manipulators. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.Index Terms-Machine learning, adaptive control, repetitive control, robot manipulators. I. INTRODUCTIONMachine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data [1]. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Machine learning, like all subjects in artificial intelligence, require cross-disciplinary proficiency in several areas, such as probability theory, statistics, pattern recognition, cognitive science, data mining, adaptive control, computational neuroscience and theoretical computer science [2]. In this paper we are focused on learning algorithms for robot manipulators. II. MACHINE LEARNING ALGORITHMSMachine learning algorithms are organized into a taxonomy based on the desired outcome of the algorithm.Supervised learning generates a function that maps inputs to desired outputs. For example, in a classification problem, the learner approximates a function mapping a vector into classes by looking at input-output examples of the function for robot manipulators [1].Unsupervised learning models a set of inputs, like clustering [1].Semi-supervised learning combines both labeled and unlabeled examples to generate an appropriate function or classifier in manipulators [3].Reinforcement learning learns how to act given an observation of the world. Every action has some impact in the environment, and the environment provides feedback in the form of rewards that guides the learning algorithm [1] and [3].Transduction tries to predict new outputs based on training inputs, training outputs, an...

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“…If such distance is greater than a preset error threshold θ then prediction is not successful. After predicting the future location of a mobile terminal, the C classifier receives feedback from the environment considering whether the prediction was successful or not, and reorganize the knowledge base accordingly [14]. In our case, the feedback is the actual 3DP observed in the terminal's movement.…”

Section: Mobility Prediction Modelmentioning

confidence: 99%

An Online Adaptive Model for Location Prediction

Anagnostopoulos

Hadjiefthymiades

2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal online clustering is adopted. We rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. We introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. A learning method is presented and evaluated. We compare ART with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed model in mobile context aware applications.

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Learning Automata - A Survey

Abstract: Abstract-Stochastic automata operating in an unknown random can be considered to show learning behavior. Tsypkin environment have been proposed earlier as models of learning. These [GT1]

Cited by 597 publications

References 35 publications

Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning

Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning

The Classification of the Applicable Machine Learning Methods in Robot Manipulators

An Online Adaptive Model for Location Prediction

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